Is unified memory on MacBook Pro M4 16GB as fast as VRAM for GPT-OSS 20B?

Not always. MacBook Pro M4 16GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

Can GPT-OSS 20B run on MacBook Pro M4 16GB?

Q: What should I upgrade first if GPT-OSS 20B feels slow on MacBook Pro M4 16GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With Q2_K

A77Great

Estimated from fit model

GPT-OSS 20B needs ~13.3 GB VRAM. MacBook Pro M4 16GB has 11.5 GB. With Q2_K quantization, expect ~18 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

GPT-OSS 20B at Q4_K_M needs 17.9 GB — too much for MacBook Pro M4 16GB (11.5 GB). Runs at Q2_K (13.3 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 17.9 GB, exceeds 11.5 GB available

17.9 GB required11.5 GB available

156% VRAM needed

6.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

9.3 tok/s

TTFT

20746 ms

Safe context

Memory

17.9 GB / 11.5 GB

Offload

40%

Memory breakdown

Weights12.8 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsGPT-OSS 20B on MacBook Pro M4 16GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers))  # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
    """Sort a list using the quicksort algorithm.

    Args:
        arr: List of integers to sort.

    Returns:
        A new sorted list.
    """
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 9.3 tok/s decode · 20.7s TTFT (warm) · 23 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 1.1 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	10.1 tok/s	10442 ms	4K
Coding	F	Too heavy	9.3 tok/s	20746 ms	4K
Agentic Coding	F	Too heavy	8.1 tok/s	34790 ms	4K
Reasoning	F	Too heavy	9.3 tok/s	24518 ms	4K
RAG	F	Too heavy	8.1 tok/s	43488 ms	4K

Quantization options

How GPT-OSS 20B (21B params) fits at each quantization level on MacBook Pro M4 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.2 GB	Low	F0
Q3_K_S	3	10.3 GB	Low	F0
NVFP4	4

Get started

Copy-paste commands to run GPT-OSS 20B on your machine.

Run

ollama run gpt-oss

Upgrade options

Hardware that runs GPT-OSS 20B well

MacBook Pro M4 32GBBudget pick

32 GB Unified (+16)

Makes the model fit on the accelerator instead of staying completely out of reach.16.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$799 MSRP

Mac mini M4 32GBBest value

32 GB Unified (+16)

Makes the model fit on the accelerator instead of staying completely out of reach.16.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,099 MSRP

Mac mini M4 64GBApple upgrade

64 GB Unified (+48)

Makes the model fit on the accelerator instead of staying completely out of reach.16.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,099 MSRP

RTX 3090 24GBBiggest leap

24 GB VRAM (+8)936 GB/s (+816)

Makes the model fit on the accelerator instead of staying completely out of reach.113.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$1,499 MSRP

Frequently asked questions

Can MacBook Pro M4 16GB run GPT-OSS 20B?

Yes, MacBook Pro M4 16GB can run GPT-OSS 20B at Q2_K quantization (Very compromised (needs ~1.1 GB host RAM)). The recommended Q4_K_M requires 17.9 GB which exceeds available memory, but at Q2_K it needs only 13.3 GB. Expected decode speed: 17.7 tok/s.

How much VRAM does GPT-OSS 20B need?

GPT-OSS 20B (21B parameters) requires approximately 17.9 GB at Q4_K_M quantization. On MacBook Pro M4 16GB, it fits at Q2_K using 13.3 GB.

What is the best quantization for GPT-OSS 20B?

The recommended quantization is Q4_K_M, but on MacBook Pro M4 16GB the best fitting quantization is Q2_K, which uses 13.3 GB.

What speed will GPT-OSS 20B run at on MacBook Pro M4 16GB?

On MacBook Pro M4 16GB, GPT-OSS 20B achieves approximately 17.7 tokens per second decode speed with a time-to-first-token of 10932ms using Q2_K quantization.

Can MacBook Pro M4 16GB run GPT-OSS 20B for coding?

For coding workloads, GPT-OSS 20B on MacBook Pro M4 16GB receives a F grade with 9.3 tok/s and 4K context.

What context window can GPT-OSS 20B use on MacBook Pro M4 16GB?

On MacBook Pro M4 16GB, GPT-OSS 20B can safely use up to 5K tokens of context at Q2_K quantization. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for MacBook Pro M4 16GB See all hardware for GPT-OSS 20B

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